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Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

This paper presents the proposal for an associative memory implemented in cellular automata for pattern recognition. Both in their learning phase and recovery, the proposed model is based on dilation and erosion operators from mathematical morphology. The model was applied to the iris plant, abalone and hepatitis databases taken from the repertoire of available databases for Machine Learning Center and Intelligent Systems at the University of California Irvine.

Agricultural productivity is an important factor for the economic development of a country. Therefore, the diagnosis of plant diseases is a field of research of utmost importance for the agricultural sector as it allows us to help recommend strategies to avoid the spread of diseases, thus reducing economic losses. Currently, with the rise of computer systems, computer systems have been developed that allow computer-assisted diagnosis in different research fields, including the agricultural sector. This work proposes the development of a methodology that allows the detection of three types of diseases in tomato leaves (late blight, tomato mosaic virus and Septoria leaf spot) by image analysis and pattern recognition. The methodology is divided into three stages: (1) segmentation of the leaf and of the lesion, (2) feature extraction using color moments and Gray Level Co-occurrence Matrix (GLCM) and (3) classification. For the segmentation process, it is proposed to use a range of pixel colors that represent healthy and diseased areas in tomato leaves using values proposed by an expert in the area of phytopathology. For the classification it is proposed to use a decision rule in which if two of the Support Vector Machines (SVM) classifiers, K Nearest Neighbors (K-NN) and Multilayer Perceptron (MLP) give the same result, then this is taken for the final decision. The result of the methodology is compared with other classifiers using the value of its accuracy and validated with cross validation.

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